The serverless-pdf-chat repository contains a sample application that allows users to ask natural language questions of any PDF document they upload. It leverages serverless services like Amazon Bedrock, AWS Lambda, and Amazon DynamoDB to provide text generation and analysis capabilities. The application architecture involves uploading a PDF document to an S3 bucket, extracting metadata, converting text to vectors, and using a LangChain to search for information related to user prompts. The application is not intended for production use and serves as a demonstration and educational tool.

Bedrock Claude ChatBot is a Streamlit application that provides a conversational interface for users to interact with various Large Language Models (LLMs) on Amazon Bedrock. Users can ask questions, upload documents, and receive responses from the AI assistant. The app features conversational UI, document upload, caching, chat history storage, session management, model selection, cost tracking, logging, and advanced data analytics tool integration. It can be customized using a config file and is extensible for implementing specialized tools using Docker containers and AWS Lambda. The app requires access to Amazon Bedrock Anthropic Claude Model, S3 bucket, Amazon DynamoDB, Amazon Textract, and optionally Amazon Elastic Container Registry and Amazon Athena for advanced analytics features.

Open Data QnA is a Python library that allows users to interact with their PostgreSQL or BigQuery databases in a conversational manner, without needing to write SQL queries. The library leverages Large Language Models (LLMs) to bridge the gap between human language and database queries, enabling users to ask questions in natural language and receive informative responses. It offers features such as conversational querying with multiturn support, table grouping, multi schema/dataset support, SQL generation, query refinement, natural language responses, visualizations, and extensibility. The library is built on a modular design and supports various components like Database Connectors, Vector Stores, and Agents for SQL generation, validation, debugging, descriptions, embeddings, responses, and visualizations.

The Generative AI Application Builder on AWS (GAAB) is a solution that provides a web-based management dashboard for deploying customizable Generative AI (Gen AI) use cases. Users can experiment with and compare different combinations of Large Language Model (LLM) use cases, configure and optimize their use cases, and integrate them into their applications for production. The solution is targeted at novice to experienced users who want to experiment and productionize different Gen AI use cases. It uses LangChain open-source software to configure connections to Large Language Models (LLMs) for various use cases, with the ability to deploy chat use cases that allow querying over users' enterprise data in a chatbot-style User Interface (UI) and support custom end-user implementations through an API.

The Intel® Geti™ SDK is a python package that enables teams to rapidly develop AI models by easing the complexities of model development and fostering collaboration. It provides tools to interact with an Intel® Geti™ server via the REST API, allowing for project creation, downloading, uploading, deploying for local inference with OpenVINO, configuration management, training job monitoring, media upload, and prediction. The repository also includes tutorial-style Jupyter notebooks demonstrating SDK usage.

The Intel® Geti™ SDK is a python package that enables teams to rapidly develop AI models by easing the complexities of model development and enhancing collaboration between teams. It provides tools to interact with an Intel® Geti™ server via the REST API, allowing for project creation, downloading, uploading, deploying for local inference with OpenVINO, setting project and model configuration, launching and monitoring training jobs, and media upload and prediction. The SDK also includes tutorial-style Jupyter notebooks demonstrating its usage.

Cluster Toolkit is an open-source software by Google Cloud for deploying AI/ML and HPC environments on Google Cloud. It allows easy deployment following best practices, with high customization and extensibility. The toolkit includes tutorials, examples, and documentation for various modules designed for AI/ML and HPC use cases.

GenMedia Creative Studio is an application showcasing the capabilities of Google Cloud Vertex AI generative AI creative APIs. It includes features like Gemini for prompt rewriting and multimodal evaluation of generated images. The app is built with Mesop, a Python-based UI framework, enabling rapid development of web and internal apps. The Experimental folder contains stand-alone applications and upcoming features demonstrating cutting-edge generative AI capabilities, such as image generation, prompting techniques, and audio/video tools.

Hugging Face AI Sheets is an open-source tool for building, enriching, and transforming datasets using AI models with no code. It can be deployed locally or on the Hub, providing access to thousands of open models. Users can easily generate datasets, run data generation scripts, and customize inference endpoints for text generation. The tool supports custom LLMs and offers advanced configuration options for authentication, inference, and miscellaneous settings. With AI Sheets, users can leverage the power of AI models without writing any code, making dataset management and transformation efficient and accessible.

This repository provides a deployment guide for utilizing Google Cloud's Generative AI tools in marketing scenarios. It includes step-by-step instructions, examples of crafting marketing materials, and supplementary Jupyter notebooks. The demos cover marketing insights, audience analysis, trendspotting, content search, content generation, and workspace integration. Users can access and visualize marketing data, analyze trends, improve search experience, and generate compelling content. The repository structure includes backend APIs, frontend code, sample notebooks, templates, and installation scripts.

Cognita is an open-source framework to organize your RAG codebase along with a frontend to play around with different RAG customizations. It provides a simple way to organize your codebase so that it becomes easy to test it locally while also being able to deploy it in a production ready environment. The key issues that arise while productionizing RAG system from a Jupyter Notebook are: 1. **Chunking and Embedding Job** : The chunking and embedding code usually needs to be abstracted out and deployed as a job. Sometimes the job will need to run on a schedule or be trigerred via an event to keep the data updated. 2. **Query Service** : The code that generates the answer from the query needs to be wrapped up in a api server like FastAPI and should be deployed as a service. This service should be able to handle multiple queries at the same time and also autoscale with higher traffic. 3. **LLM / Embedding Model Deployment** : Often times, if we are using open-source models, we load the model in the Jupyter notebook. This will need to be hosted as a separate service in production and model will need to be called as an API. 4. **Vector DB deployment** : Most testing happens on vector DBs in memory or on disk. However, in production, the DBs need to be deployed in a more scalable and reliable way. Cognita makes it really easy to customize and experiment everything about a RAG system and still be able to deploy it in a good way. It also ships with a UI that makes it easier to try out different RAG configurations and see the results in real time. You can use it locally or with/without using any Truefoundry components. However, using Truefoundry components makes it easier to test different models and deploy the system in a scalable way. Cognita allows you to host multiple RAG systems using one app. ### Advantages of using Cognita are: 1. A central reusable repository of parsers, loaders, embedders and retrievers. 2. Ability for non-technical users to play with UI - Upload documents and perform QnA using modules built by the development team. 3. Fully API driven - which allows integration with other systems. > If you use Cognita with Truefoundry AI Gateway, you can get logging, metrics and feedback mechanism for your user queries. ### Features: 1. Support for multiple document retrievers that use `Similarity Search`, `Query Decompostion`, `Document Reranking`, etc 2. Support for SOTA OpenSource embeddings and reranking from `mixedbread-ai` 3. Support for using LLMs using `Ollama` 4. Support for incremental indexing that ingests entire documents in batches (reduces compute burden), keeps track of already indexed documents and prevents re-indexing of those docs.

AWS AI Stack is a full-stack boilerplate project designed for building serverless AI applications on AWS. It provides a trusted AWS foundation for AI apps with access to powerful LLM models via Bedrock. The architecture is serverless, ensuring cost-efficiency by only paying for usage. The project includes features like AI Chat & Streaming Responses, Multiple AI Models & Data Privacy, Custom Domain Names, API & Event-Driven architecture, Built-In Authentication, Multi-Environment support, and CI/CD with Github Actions. Users can easily create AI Chat bots, authentication services, business logic, and async workers using AWS Lambda, API Gateway, DynamoDB, and EventBridge.

MCP2Lambda is a server that acts as a bridge between MCP clients and AWS Lambda functions, allowing generative AI models to access and run Lambda functions as tools. It enables Large Language Models (LLMs) to interact with Lambda functions without code changes, providing access to private resources, AWS services, private networks, and the public internet. The server supports autodiscovery of Lambda functions and their invocation by name with parameters. It standardizes AI model access to external tools using the MCP protocol.

OpenRepoWiki is a tool designed to automatically generate a comprehensive wiki page for any GitHub repository. It simplifies the process of understanding the purpose, functionality, and core components of a repository by analyzing its code structure, identifying key files and functions, and providing explanations. The tool aims to assist individuals who want to learn how to build various projects by providing a summarized overview of the repository's contents. OpenRepoWiki requires certain dependencies such as Google AI Studio or Deepseek API Key, PostgreSQL for storing repository information, Github API Key for accessing repository data, and Amazon S3 for optional usage. Users can configure the tool by setting up environment variables, installing dependencies, building the server, and running the application. It is recommended to consider the token usage and opt for cost-effective options when utilizing the tool.

Unity Catalog is an open and interoperable catalog for data and AI, supporting multi-format tables, unstructured data, and AI assets. It offers plugin support for extensibility and interoperates with Delta Sharing protocol. The catalog is fully open with OpenAPI spec and OSS implementation, providing unified governance for data and AI with asset-level access control enforced through REST APIs.

VectorVein is a no-code AI workflow software inspired by LangChain and langflow, aiming to combine the powerful capabilities of large language models and enable users to achieve intelligent and automated daily workflows through simple drag-and-drop actions. Users can create powerful workflows without the need for programming, automating all tasks with ease. The software allows users to define inputs, outputs, and processing methods to create customized workflow processes for various tasks such as translation, mind mapping, summarizing web articles, and automatic categorization of customer reviews.

The serverless-pdf-chat repository contains a sample application that allows users to ask natural language questions of any PDF document they upload. It leverages serverless services like Amazon Bedrock, AWS Lambda, and Amazon DynamoDB to provide text generation and analysis capabilities. The application architecture involves uploading a PDF document to an S3 bucket, extracting metadata, converting text to vectors, and using a LangChain to search for information related to user prompts. The application is not intended for production use and serves as a demonstration and educational tool.

Docling simplifies document processing, parsing diverse formats including advanced PDF understanding, and providing seamless integrations with the general AI ecosystem. It offers features such as parsing multiple document formats, advanced PDF understanding, unified DoclingDocument representation format, various export formats, local execution capabilities, plug-and-play integrations with agentic AI tools, extensive OCR support, and a simple CLI. Coming soon features include metadata extraction, visual language models, chart understanding, and complex chemistry understanding. Docling is installed via pip and works on macOS, Linux, and Windows environments. It provides detailed documentation, examples, integrations with popular frameworks, and support through the discussion section. The codebase is under the MIT license and has been developed by IBM.

This repository contains shared codebase for two projects: gcloud-aio and gcloud-rest. gcloud-aio is built for Python 3's asyncio, while gcloud-rest is a threadsafe requests-based implementation. It provides clients for Google Cloud services like Auth, BigQuery, Datastore, KMS, PubSub, Storage, and Task Queue. Users can install the library using pip and refer to the documentation for usage details. Developers can contribute to the project by following the contribution guide.

Airbroke is an open-source error catcher tool designed for modern web applications. It provides a PostgreSQL-based backend with an Airbrake-compatible HTTP collector endpoint and a React-based frontend for error management. The tool focuses on simplicity, maintaining a small database footprint even under heavy data ingestion. Users can ask AI about issues, replay HTTP exceptions, and save/manage bookmarks for important occurrences. Airbroke supports multiple OAuth providers for secure user authentication and offers occurrence charts for better insights into error occurrences. The tool can be deployed in various ways, including building from source, using Docker images, deploying on Vercel, Render.com, Kubernetes with Helm, or Docker Compose. It requires Node.js, PostgreSQL, and specific system resources for deployment.

aiohttp_security is a library that provides identity and authorization for aiohttp.web. It offers features for handling authorization via cookies and supports aiohttp-session. The library includes examples for basic usage and database authentication, along with demos in the demo directory. For development, the library requires installation of specific requirements listed in the requirements-dev.txt file. aiohttp_security is licensed under the Apache 2 license.

EvoMaster is an open-source AI-driven tool that automatically generates system-level test cases for web/enterprise applications. It uses Evolutionary Algorithm and Dynamic Program Analysis to evolve test cases, maximizing code coverage and fault detection. It supports REST, GraphQL, and RPC APIs, with whitebox testing for JVM-compiled APIs. The tool generates JUnit tests in Java or Kotlin, focusing on fault detection, self-contained tests, SQL handling, and authentication. Known limitations include manual driver creation for whitebox testing and longer execution times for better results. EvoMaster has been funded by ERC and RCN grants.

This is the official Clarifai gRPC Python client for interacting with their recognition API. Clarifai offers a platform for data scientists, developers, researchers, and enterprises to utilize artificial intelligence for image, video, and text analysis through computer vision and natural language processing. The client allows users to authenticate, predict concepts in images, and access various functionalities provided by the Clarifai API. It follows a versioning scheme that aligns with the backend API updates and includes specific instructions for installation and troubleshooting. Users can explore the Clarifai demo, sign up for an account, and refer to the documentation for detailed information.

Gemini ChatUp is a chat application utilizing the Google GeminiPro API Key. It supports responsive layout and can store multiple sets of conversations with customizable parameters for each set. Users can log in with a test account or provide their own API Key to deploy the feature. The application also offers user authentication through Edge config in Vercel, allowing users to add usernames and passwords in JSON format. Local deployment is possible by installing dependencies, setting up environment variables, and running the application locally.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.